Senior Data Scientist

Key Responsibilities

• Design, develop, and deploy end-to-end machine learning and statistical models to address critical institutional challenges (e.g., student performance trajectories, resource allocation, and operational efficiency).
• Audit, monitor, and continuously improve existing production models, ensuring their accuracy, reliability, and long-term scalability.
• Act as a primary mentor to data analysts and engineers, conducting code reviews, organizing internal knowledge-sharing sessions, and disseminating current data science methodologies across the team.
• Architect efficient data extraction workflows and construct complex, scalable queries to manipulate large datasets for feature engineering and analysis.
• Work alongside data engineers and product owners to establish clean data pipelines and translate model outputs into actionable dashboards or software features.

What We Are Looking For (Requirements)

• Experience: 5+ years of professional experience in data science, predictive modeling, or advanced analytics, with a proven track record of bringing machine learning models into production environments.
• Expert Python Proficiency: Deep expertise in Python and its core data science ecosystem (including pandas, NumPy, scikit-learn, and related framework libraries) for building robust, clean, and reusable code.
• Advanced SQL Skills: Exceptional ability to write, optimize, and debug complex SQL queries against large relational databases or modern cloud data warehouses.
• Core Data Science Fundamentals: Strong command of statistical modeling, regression techniques, classification algorithms, and experimental design (A/B testing).
• Mentorship Mindset: Prior experience or a strong demonstrated desire to guide, mentor, and upskill junior team members while establishing technical standards.

Nice to Haves (Preferred Qualifications)

• Statistical Programming in R: Familiarity with R for exploratory data analysis, prototyping, or specific statistical packages.
• Cloud & MLOps Tools: Exposure to cloud data infrastructure (such as AWS, GCP, or Snowflake) and basic ML orchestration/versioning tools (e.g., MLflow, Airflow, Git).
• Domain Context: Prior experience working within education, non-profits, or public sector datasets, though a diverse background across other industries is highly valued.

Pay

Exact compensation may vary based on skills and experience. This position is not bonus eligible.

Compensation Range: $180,000 - $200,000 USD